Higher Education Strategy Associates

Category Archives: teaching

August 04

Summer Updates from Abroad (2): The UK Teaching Excellence Framework

The weirdest – but also possibly most globally consequential – story from this year’s higher education silly season comes from England.  It’s about something called a “Teaching Excellence Framework”.

Now, news of nationally-specific higher education accountability mechanisms don’t often travel.  Because, honestly, who cares?  It’s enough trouble keeping track of accountability arrangements in one’s own country.  But there are few in academia, anywhere, who have not heard about the UK’s Research Excellence Framework (or its nearly-indistinguishable predecessor, the Research Assessment Exercise).  There is scarcely a living British academic who has travelled abroad in the last two decades without regaling foreign colleagues with tales of this legendary process, usually using words like “vast”, “bureaucratic”, “walls full of filing cabinets”, etc.  So news that the country may be looking at creating a second such framework, related to teaching, is sure to strike many as some sort of Orwellian joke.

But no, this government is serious.  It’s fair to say that the government was somewhat disappointed that its de-regulation of tuition fees did not force institutions to focus more on teaching quality.  With the market having failed in that task, they seem to be retreating to good old-fashioned regulation, mixed with financial incentives.

The idea – and, at the moment, it’s still just a pretty rough idea – is rather simple: institutions should be rated on the quality of their teaching.  But there are two catches: first, how do you measure it?  And second, what are the rewards for doing well?

The first of these seems to be up in the air.  Although the government has committed to the principle of assessing teaching at the institutional level, it genuinely seems to have not thought through in the least how it intends to achieve this.  There are a lot of options here: one could simply look at use of resources and presence of qualifications: student/teacher ratios, number of profs who have actually sought teaching qualifications, etc.  One could go the survey route, and ask students how they feel about teaching; one could also go the peer assessment route, and have profs rate each others’ teaching.  Or there’s the “learning gain” model, used by the Collegiate Learning Assessment, which was part of the AHELO system (from which, by the way, the UK has now officially withdrawn).  Of course, everyone knows that most of these measurements are either untested, or can be gamed, so there’s some fear that what the government really wants to do is to rely on – what might generously be called – lowest-common denominator statistics; namely, employment and income data.

Why might they want to do something this bell-ended, when everyone knows income is tied most closely to fields of study?  Well, the clue is in the rewards.  British universities have – as universities do – recently been clamouring for more money.  But according to this government, there is no more money to be had; in fact, at about the same time they announced the new excellence framework, they also announced a £150 million cut to the basic teaching grant, spread over two years.  So the proposed reward for good teaching is the ability to charge higher fees (so much for de-regulation… ) But as I explained a couple weeks backraising tuition doesn’t help much because, thanks to high debt and a generous loan forgiveness system, somewhere between 60 and 80% of any extra charges at the margin will end up on the public books circa 2048, anyway. 

But… if you only increase tuition at schools where income is the highest, the likelihood is that you will get a higher proportion of graduates earning enough to pay back their loans, over time.  And hence less money will need to be forgiven.  And hence this might not actually cost so much.  Which is why there is an incentive for government to do the wrong thing here.

Still, on the off-chance the government gets this initiative at least partially right, the impact could be global.  Governments all over the world are trying to get institutions to pay more attention to teaching; expect a lot of imitators if the results of this exercise look even half-promising.  Stay tuned.

April 08

ATMs and the Future of Education

I recently came across a fascinating counterintuitive piece of trivia in Timothy Taylor’s Conversable Economist blog.  At the time ATMs were introduced in 1980, there were half a million bank tellers in America.  How many were there 30 years later, in 2010?  Answer: roughly 600,000.  Don’t believe me?  See the data here.

Most people to whom I’ve told this story tend to get confused by this.  ATMs are one of the classic examples about how technology destroys “good middle class jobs”.  And so the first instinct many people have when confronted with this information is to try and defend the standard narrative – usually with something like “ah, but population growth, so they still took away jobs that could have existed”.  This is wrong, though.  When we look at manufacturing, we see absolute declines in jobs due to (among other things) automation.  With ATMs, however, all we see is a change in the rate of growth.

The key thing to grasp here is that the machines did not put the tellers out of business; rather, they modified the nature of bank telling.  To quote Taylor, “tellers evolved from being people who put checks in one drawer and handed out cash from another drawer to people who solved a variety of financial problems for customers”.

There’s an important truth here about the way skill-use evolves in the economy.  When most people think about technological change and its impacts on skills, they initially tend to presume “more machines → high tech → more tech skills needed → more STEM”.  But actually this is, at best, half the story.  Yes, new job categories are springing up in technical areas that require new forms of training.  But the more important news is that older job categories evolve into new ones with different kinds of requirements, and requiring a different skill set.  And in most cases, those new skills are – as in our bank teller example – about problem-solving.

Now, as a society, every time we see job requirements changing, our instinct is to keep kids in school longer.  But: a) pretty soon cost constraints put a ceiling on that strategy; and, b) this approach is of limited usefulness if all you’re doing is teaching the same old things for longer.

At a generic level, it’s not hard to teach in such a way that you’re giving students necessary skills to thrive in the future labour market.  Most programs, at some level, teach problem-solving (identifying a problem, synthesizing data about it, coming up with possible solutions, evaluating them, and coming up with a solution), although not all of them test for them explicitly, or explain to students how these skills are likely to be applied later on.  More could be done with respect to encouraging teamwork and interpersonal skills, but these aren’t difficult to add (although having the will to add them is something different).

The more difficult problem has to do with understanding where technology is likely to replace jobs and where it is likely to modify them.  What do driverless cars mean for the delivery business?  At a guess, it means an expanded market for the delivery of personalized services during commuting time.  Improved automatic diagnostic technology or robot pharmacists?  More demand for health professionals to dispense lifestyle and general health counselling.  Increased automation in legal affairs?  Less time on research means more time for, and emphasis on, negotiation.

I could go on, but I won’t.  The point, as Tyler Cowen makes in Average is Over (a book whose implications for higher education have been criminally under-examined) is that the future in many fields belongs to people who can best blend human creativity with the power of computers.  And so the relevant question for universities is: to what extent are you monitoring technology trends and thinking about how they will change what you teach, how you teach it, and how you evaluate it?  Or, put differently: to what extent are your curricula “future-ready”?

In too many cases, the answers to these questions land somewhere between “not very much” and “not at all”.  As a sector, there is some homework to be done here.

February 11

Who Owns Courses?

After the preposterous CAUT report on the University of Manitoba’s Economics Department was released, President David Barnard offered a wonderfully robust and thought-provoking refutation of CAUT’s accusations.

One of the most interesting observations Barnard makes relates to a specific incident from the report, namely the request by a departmental council to review an existing Health Economics course after having approved a new Economic Determinants of Health Course taught by the same professor.  CAUT viewed this as a violation of the professor’s academic freedom (basically – she/he can teach whatever she/he likes).

In an age when we are all intensely aware of intellectual property rights issues, we have, over time, come to focus on the professor’s role as a creator of content.  And this is absolutely right.  The way in which Economics Macro 300 or Organizational Behaviour 250 gets taught is a reflection of a professor’s lifetime of scholarship, and many hundreds of hours of hard work in creating a pedagogy and syllabus that conveys the necessary information to students.  The idea that this “belongs” to anyone other than the professor is ridiculous – which is why there have been such fierce battles over the terms of universities’ involvement with private for-profit companies, like Coursera, with respect to online education.

Barnard responds to this line of thinking by reminding us of a very important truth: Macro 300 and OB 250 exist independently of the professors who currently teach them.  When they are approved by Senate, they become the property of the university as a whole (with the department in which the course is situated taking special responsibility).  After the incumbent of a particular course retires or leaves, someone else will be asked to takeover.  The course, in this sense, is eternal and communal.  It does not “belong” to the professor.

There’s an obvious tension here between the way a course gets taught (owned by the prof) and the course objectives and outcomes (owned by the university).  Usually – at least in Canada and the United States – we solve the problem by always leaning in favour of the professor.  Which is certainly the easier option.  However, this attitude, which gives total sovereignty to professors at the level of the individual course, inevitably leads to programs become disjointed –  especially in Arts and Sciences.  Students end up missing key pieces of knowledge, or have to learn it and re-learn it two or three times.

Universities own courses in the sense that a course is a building block towards a degree, (which the university very definitely owns – its entire existence is predicated on being a monopoly provider of degrees).  As a result, course objectives, how a course fits into the overall program goals, course assessment guidelines, and course delivery mechanisms (online, blended, or in-person) are all legitimately in the hands of the university and its academic decision-making bodies.  The actual syllabus – that is, what material gets taught in pursuit of the objectives – and the pedagogical methods used is what belongs to the professor.

The problem here is that, in Arts and Science at least (less so elsewhere), our smorgasbord thinking about curriculum makes us prone to assuming that courses stand alone, and do not contribute to a larger programmatic structure.  Hence the widespread fallacy that professors “own” courses, when the reality is that courses are a shared enterprise.

January 23

Classroom Economics (The End)

So we spent Monday looking at the economic basics of classroom and teaching loads, and Tuesday looking at how difficult it is to improve the situation by increases in tuition or government grants.  Wednesday we saw that reducing average academic compensation (presumably via increasing the proportion of credits taught by adjuncts) can be quite effective in reducing teaching loads, while on Thursday we saw how trying to achieve a similar effect through attacking costs other than academic compensation would require enormously painful – and probably unrealistic – cuts.

What can we conclude from all this?

There is no silver bullet here.  You can’t solve everything on the revenue side because governments: i) aren’t going to fork over the stonking huge amounts of money required to change things; ii) aren’t going to permit large tuition increases; and, iii) at some point are going to put limits on the extent to which universities can escape domestic fiscal problems by becoming finishing schools for the Asian middle class.  At the same time, you can’t solve everything by decreasing average academic wages because: i) tenure; ii) unions; and, iii) casualization can’t go on indefinitely.  Finally, you can’t solve everything by cutting “fat” on the non-academic side because the size of the bloodletting would simply be too big.

So, realistically, the solution to keeping teaching loads (and hence class sizes) manageable is to work at the margins on all three, at once.  The income one is probably the easiest: even if government does not have more money, it could (as I argued back here) allow tuition to rise without students being unduly affected if it simply reformed student aid to make it more efficient and transparent.

On non-academic costs, vigilance is key.  Costs need to be kept in check.  There is a need to continually become more efficient – which probably means looking more seriously at outsourcing certain functions. Bits of IT come to mind, as do bookshops.

On academic salaries, there’s no big secret about what needs to be done.  Every time wages increase, universities either have to get more income, or increase the number of sessionals, or raise teaching loads.  That’s simple arithmetic.  To the extent an institution can keep enrolments up and get a little bit more money per student, on average, the situation can stay relatively stable indefinitely (though it isn’t going to get any better).

Where this gets tricky is where student numbers – and hence income – start to fall.  We didn’t explore that this week because our equation – X = aϒ/(b+c) – assumes that there is budget balance.  But when enrolment drops, expenditure has to drop in the medium term because the lack of students means you can’t release the pressure by increasing teaching loads.

So when you see the number of applicants to an institution drop by, say, 20% (as first-choice applications have now done at Windsor) over two years, you start to worry.  Without the option to increase loads, expenditures have to fall, and as we’ve seen, the least disruptive way to do that is to increase sessionals.  But since tenure exists and you can’t force out a professor and replace them with a sessional, that’s a marginal solution at best.  Academic compensation will have to fall: either through wage freezes, pension changes, or a reduction in the number of academic positions.  Either that or the institution will close.

There’s no sinister conspiracy here, no evil administrative plots.  It’s just math.  More people should pay attention to it.

January 21

Classroom Economics (Part 3)

(If you’re just tuning in today, you may want to catch up on Part 1 and Part 2)

Back to our equation: X = aϒ/(b+c), where “X” is the total number of credit hours a professor must teach each year (a credit hour here meaning one student sitting in one course for one term), “ϒ” is average compensation per professor, “a” is the overhead required to support each professor, “b” is the government grant per student credit hour, and “c” is the tuition revenue per credit hour.

I noted in Part 1 of this series that most profs don’t actually teach the 235 credit hours our formula implied. Partly that’s because teaching loads aren’t distributed equally.  Imagine a department of ten people, which would need to teach 2350 credit hours in order to cover its costs.  If just two people teach the big intro courses and take on 500 credit hours apiece, the other 8 will be teaching a much more manageable 169 credit hours (5 classes of under 35 students for those teaching 3/2).

Now, while I’m talking about class size, you’ll notice that this concept isn’t actually a factor in our equation – only the total number of credit hours required to be taught.  You can divide ‘em up how you want.  Want to teach 5 courses a year?  Great.  Average class size will be 47.  Want to teach four courses?  No sweat, just take 59 students per class instead.  It’s up to you.

When you hear professors complain about increased class sizes, this is partly what’s going on.  As universities have reduced professors’ teaching loads (to support research, natch) without reducing the number of students, the average number of students per class has risen.  That has nothing to do with underfunding or perfidious administrators; it’s just straight arithmetic.

But there is a way to get around this.  Let’s say a university lowers its normal teaching load from 3/2 to 2/2, as many Canadian institutions have done in the last two decades.  As I note above, there is no necessary financial cost to this: just offer fewer, larger courses.  Problem is, no university that has gone down this path has actually reduced its course offerings by the necessary 20% to make this work.  Somehow, they’re still offering those courses.

That “somehow” is sessional lecturers, or adjuncts if you prefer.  They’ll teach a course for roughly a third of what a full-time prof will.  So their net effect on our equation is to lower the average price of academic labour.  Watch what happens when we reduce teaching loads from 3/2 to 2/2, and give that increment of classes over to adjuncts.

(.8*150,000) + (.2*50,000) = $130,000

X= 2.27($150,000)/($600+$850) = 235

X= 2.27(130,000)/($600+$850) = 195

The alert among you will probably note that the fixed cost nature of “a” means that it would likely rise somewhat as ϒ falls, so this is probably overstating the fall in teaching loads a bit.  But still, this result is pretty awesome.  If you reduce your faculty teaching load, and hand over the difference to lower-paid sessionals, not only do you get more research, but the average teaching load also falls significantly.  Everyone wins!  Well, maybe not the sessionals, but you get what I mean.

This underlines something pretty serious: the financial problems we have lay much more on the left side of the equation than on the right side.  However much you think professors deserve to be paid, there’s an iron triangle of institutional income, salaries, and credit hours that cannot be escaped.  If you can’t increase tuition, and more government money isn’t forthcoming, then you either have to accept higher teaching loads or lower average salaries.  And if wage rollbacks among full-time staff isn’t in the cards, then average costs are going to be reduced through increased casualization.  Period.

Or almost, anyway. To date we’ve focused just on ϒ – but what about “a”?  Can’t we make that coefficient smaller somehow?

Good question.  More tomorrow.

January 20

Classroom Economics (Part 2)

Yesterday, I introduced the equation X = aϒ/(b+c) as a way of setting overall teaching loads. Let’s now use this to understand how funding parameters drive overall teaching loads.

Assume the following starting parameters:







Where a credit hour = 1 student in 1 class for 1 semester.

Here’s the most obvious way it works.  Let’s say the government decides to increase funding by 10%, from $600 to $660 (which would be huge – a far larger move than is conceivable, except say in Newfoundland at the height of the oil boom).  Assuming no other changes – that is, average compensation and overhead remain constant – the 10% increase would mean:

X= 2.27($150,000)/($600+$850) = 235

X= 2.27($150,000)/($660+$850) = 225

In other words, a ten percent increase in funding and a freeze on expenditures would reduce teaching loads by about 4%.  Assuming a professor is teaching 2/2, that’s a decrease of 2.5 students per class.  Why so small?  Because in this scenario (which is pretty close to the current situation in Ontario and Nova Scotia), government funding is only about 40% of operating income.  The size of the funding increase necessary to generate a significant effect on teaching loads and class sizes is enormous.

And of course that’s assuming no changes in other costs.  What happens if we assume a more realistic scenario, one in which average salaries rise 3%, and overhead rises at the same rate?

X= 2.27($154,500)/($660+$850) = 232

In other words, as far as class size is concerned, normal (for Canada anyway) salary increases will eat up about 70% of a 10% increase in government funding.  Or, to put it another way, one would normally expect a 10% increase in government funding to reduce class sizes by a shade over 1%.

Sobering, huh?

OK, let’s now take it from the other direction – how big an income boost would it take to reduce class sizes by 10%?  Well, assuming that salary and other costs are rising by 3%, the entire right side of the equation (b+c) would need to rise by 14.5%.  That would require an increase in government funding of 35%, or an increase in revenues from students of 25% (which could either be achieved through tuition increases, or a really big shift from domestic to international enrolments), or some mix of the two; for instance, a 10% increase in government funds and a 17% increase in student funds.

That’s more than sobering.  That’s into “I really need a drink” territory.  And what makes it worse is that even if you could pull off that kind of revenue increase, ongoing 3% increases in salary and overhead would eat up the entire increase in just three years.

Now, don’t take these exact numbers as gospel.  This example works in a couple of  low-cost programs (Arts, Business, etc.) in Ontario and Nova Scotia (which, to be fair, represent half the country’s student body), but most programs in most provinces are working off a higher denominator than this, and for them it would be less grim than I’m making out here.  Go ahead and play with the formula with data from your own institution and see what happens – it’s revealing.

Nevertheless, the basic problem is the same everywhere.  As long as costs are increasing, you either have to get used to some pretty heroic revenue assumptions (likely involving significant tuition increases) or you have to get used to the idea of ever-higher teaching loads.

So what are the options on cost-cutting?  Tune in tomorrow.

July 07

How to Measure Teaching Quality

One of the main struggles with measuring performance in higher education – whether of departments, faculties, or institutions – is how to measure the quality of teaching.

Teaching does not go entirely unmeasured in higher education.  Individual courses are rated by students through course evaluation surveys, which occur at the end of each semester.  The results of these evaluations do have some bearing on hiring, pay, and promotion (though how much bearing varies significantly from place to place), but these data are never aggregated to allow comparisons of quality of instruction across departments or institutions.  That’s partly because faculty unions are wary about using individual professors’ performance data as an input for anything other than pay and promotion decisions, but it also suits the interests of the research-intensive universities who do not wish to see the creation of a metric that would put them at a disadvantage vis-a-vis their less-research-intensive brethren (which is also why course evaluations differ from one institution to the next).

Some people try to get around the comparability issue by asking students about teaching generally at their institution.  In European rankings (and Canada’s old Globe and Mail rankings), many of which have a survey component, students are simply asked questions about the quality of courses they are in.  This gets around the issue of using course evaluation data, but it doesn’t address a more fundamental problem, which is that a large proportion of academic staff essentially believes the whole process is inherently flawed because students are incapable of knowing quality teaching when they see it.  There is a bit of truth here: it has been established, for instance, that teachers who grade more leniently tend to get better course satisfaction scores.  But this is hardly a lethal argument.  Just control for average class grade before reporting the score.

It’s not as though there isn’t a broad consensus on what makes for good teaching.  Is the teacher clear about goals and expectations?  Does she/he communicate ideas effectively?  Is he or she available to students when needed?  Are students challenged to learn new material and apply this knowledge effectively?  Ask students those kinds of questions and you can get valid, comparable responses.  The results are more complicated to report than a simple satisfaction score, sure – but it’s not impossible to do so.  And because of that, it’s worth doing.

And even the simple questions like “was this a good course” might be more indicative than we think.  The typical push-back is “but you can’t really judge effectiveness until years later”.  Well, OK – let’s test a proposition.  Why not just ask students about a course they took a few years ago, and compare it with the answers they gave in a course evaluation at the time?  If they’re completely different, we can indeed start ignoring satisfaction types of questions.  But we might find that a good result today is in fact a pretty good proxy for results in a few years, and therefore we would be perfectly justified in using it as a measure of teaching quality.

Students may be inexperienced, but they’re not dumb.  We should keep that in mind when dismissing the results of teaching quality surveys.

June 09

Teaching Load Versus Workload

I often get into discussions that go like this:

Me: Over time, the number of classes each professor teaches has gone down.  Places where people used to teach 3/2 (three classes one term, two the other) now teach 2/1.  Places where 4/3 or even 4/4 were common are now 3/2.   This has been one of the main things making higher education more expensive in Canada.

Someone else (usually a prof): Yeah, but classes are so much larger now than they used to be.

Me: Do you not think that teaching fewer classes maybe the cause of higher average class size?  Do you think that if everyone taught more classes average class size would fall?

(nota bene: This isn’t the whole story, obviously.  Student-staff ratios have gone up to such a degree that even if profs were teaching the same number of courses, numbers would still be up a bit.  Though how much is hard to say, because of the changing use of sessional lecturers.)

Someone else: Does it matter?  Same number of students, same amount of work.

Me: Is it?  Are three classes of fifty students actually the same amount as five classes of thirty students?  Doesn’t less class prep time more than make up for the increase in marking?

Someone else: Um, well, yeah.  Probably.  But we’re still doing lots of committee work!  And tenure requirements have become much more punishing than they used to be!  And those teaching loads don’t count graduate student supervisions.

Me: No doubt, committee work can take up a lot of time – though much of it exists simply to make the university less effective.  But that research one – that’s not distributed equally across the university, is it? I mean, we know that the pace of publication falls pretty quickly after tenure is granted (see figure 3 of this PPP article by Herb Emery).  And not all university research is of the same quality: Well over 10% of all Canadian faculty (24% in the humanities) have never had a publication cited by anyone else (HESA research, which we demonstrated back here).

Someone else:  And graduate supervision?

Me: Fair point.  But graduate supervision is all over the place.  Supervising a PhD in Science tends to be more intensive than in Arts.  And course-based Masters’ student are increasingly more like undergraduates than doctoral students in the loads they bring.  Hard to measure.

Someone else: But shouldn’t all this be measured?

Me: Of course.  But notice how Canadian university Collective Bargaining Agreements avoid the question of overall workload, even though they often get really specific about teaching loads.  Universities don’t want to measure this stuff because it would expose how many profs are working way too hard, and unions don’t want to measure this stuff because it would expose how many profs aren’t.    Look how hard both sides worked to discredit the HEQCO paper on professorial productivity, which posed exactly that question.

Someone else: is this ever going to change?

Me: Governments could put pressure on institutions to actually enforce the bits of the CBAs that require faculty to actually do the hard-to-measure stuff (committee work, research).  Junior staff could make more of a fuss within the unions to start ensuring equal treatment of workloads within the bargaining unit.  Short of that, no.

Someone else: Aren’t you a bit cynical?

Me: Around here, hard not to be.

March 13

Teaching Loads, Fairness, and Productivity

It’s been a long time since I’ve been as disappointed by an article on higher education as I was by the Star’s coverage of the release of the new HEQCO paper on teaching and research productivity.  A really long time.

If you haven’t read the HEQCO paper yet, do so.  It’s great.  Using departmental websites, the authors (Linda Joncker and Martin Hicks) got a list of people teaching in Economics, Chemistry, and Philosophy at ten Ontario universities.  From course calendars, Google scholar, and tri-council grant databases, they were able to work out each professor’s course load, and whether or not they were “research active” (i.e. whether they had either published something or received a tri-council grant in the past three years).  On the basis of this, they could work out the teaching loads of profs who were research-active vs. those who were not (except in Philosophy, where they reckoned they couldn’t publish the data because there simply weren’t that many profs who met their definition of being research-active).  Here’s what they found:

Annual Course Load by Research Active Status














To be clear, one course here is actually a half course.  So the finding that “non-research-active” professors teach less than one course extra means that there are, in fact, a heck of a lot of non-research-active profs who teach no extra courses, and who teach exactly the same amount as professors who are research active.

For reasons of fairness as much as  productivity, this seems like a result worth discussing, no?  And yet – here’s where the disappointment comes in – that doesn’t appear to be where the main actors in this little drama want to go with the story.  Rather, they appear to want to make irrelevant asides about the study itself.

Now I say “appear” because it’s possible they have more nuanced views on the subject, and the Star just turned the story into a he-said/she-said.  I want to give them the benefit of the doubt, because the objections printed by the Star are frankly ludicrous.  They amount to the following:

1)      Teaching involves more than classroom time, it’s preparation, grading, etc.  True, but so what?  The question is whether profs who don’t produce research should be asked to teach more.  The question of what “teaching” consists of is irrelevant.

2)      Number of courses taught is irrelevant – what matters is the number of students taught.  This is a slightly better argument, though I think most profs would say that the number of courses is a bigger factor in workload than the number of students (4 classes of 30 students is significantly harder than 3 of 40).  But for this to be a relevant argument, you’d need to prove that the profs without a research profile were actually teaching systematically larger classes than their research-active counterparts.  There’s no evidence either way on this point, though I personally would lay money against it.

Here’s the deal: you can quibble with the HEQCO data, but it needs to be acknowledged: i) that data could be better, but that it is institutions themselves who hold the data and are preventing this question from being examined in greater depth; and, ii) that this is the one of the best studies ever conducted on this topic in Canada.  Kvetching about definitions is only acceptable from those actively working to improve the data and make it public.  Anyone who’s kvetching, and not doing that, quite frankly deserves to be richly ignored.

March 10

Could We Eliminate Sessionals if We Wanted To?

Last week, when I was writing about sessionals, I made the following statement:

“Had pay levels stayed constant in real terms over the last 15 years, and the surplus gone into hiring, the need for sessionals in Arts & Science would be practically nil”.

A number of you wrote to me, basically calling BS on my statement.  So I thought it would be worthwhile to show the math on this.

In 2001-02, there were 28,643 profs without administrative duties in Canada, collectively making $2.37 billion dollars, excluding benefits.  In 2009-10, there were 37,266 profs making $4.29 billion, also excluding benefits.  Adjusting for inflation, that’s a 56% increase in total compensation – but, of course, much of that is taken up by having more profs.  If we also control for the increase in the number of professors, what we have left is an increase of 18.8%, or $679 million (in 2009 dollars).

How many new hires could you make with that?  Well, the average assistant prof in 2009 made $90,000.  So, simple math would suggest that 7,544 new assistant profs could have been hired for that amount.  That means that had professors’ salaries stayed even in real terms, universities could have hired 16,347 new staff in that decade, instead of the 8,803 they actually did.

(Okay, I’m oversimplifying a bit.  There are transaction costs to landing new professors.  And hiring that many young profs all at once would just be storing up financial chaos 5-15 years down the road, as they gain in seniority.  So $679 million probably wouldn’t buy you that many new profs.  But on the other hand, if you were doing some hiring, you’d spend less money on sessionals, too, so it’s probably not far off.)

Would that number of new hires have eliminated the need for sessionals?  Hard to say, since we have no data either on the number of sessionals, or the number of courses they collectively teach.  What we can say is that if 7,500 professors had been hired, the student:faculty ratio would have fallen from 25:1 to 22:1, instead of rising – as, in fact, it did – to 27:1. That’s a pretty significant change no matter how you slice it.

(The question remains, though: would you want to give up sessionals, even if you could?  As I pointed out last week, in many programs sessionals perform a vital role of imparting practical, real-world experience to students.  And even where that’s not their primary function, they act as swing labour, helping institutions cope with sudden surges of students in particular fields of study.  They have their uses, you know.)

Now, I’m not suggesting that professors should have foregone all real wages increases over a decade, in order to increase the size of the professoriate.  But I am suggesting that universities have made some choices in terms of pay settlements that has affected their ability to hire enough staff to teach all the students they’ve taken on.  The consequence – as I noted before – is more sessionals.  But it very definitely did not need to be that way.

Page 1 of 212